Statistical Relational AI (StaRAI) is a branch of Artificial Intelligence lying at the intersection between statistical and logical methods, applied to relational data. This class will cover the most common types of tasks considered by the StaRAI methods.

Materials used in the class come from a workshop conducted by Marco Lippi at ACAI'2018 summer school in Ferrara.

Questions:

1. What is hidden under the term “relational data”?
2. Could modern “deep” learning methods work be used in the same context?

Given a relational model of a domain (e.g. graph of connections in the social network) we have to learn how to predict connection between nodes in similar networks.

Questions:

1. What types of networks can we spot in real life?
2. What are the possible applications of the link predictor?
3. What does “similar network” mean? How can we validate the predictor?
4. What learning features can be found in the network?

Let assume we have a very tiny network as shown on the right. In this problem all links are undirected and unlabeled. Nodes have labels shown using different colors. Our ask is to train a link predictor using Problog. In case somebody forgot Problog installation is fairly easy given a working Python environment (pip install problog and optionally problog install on Linux). In case it wasn't simple enough, one can try to use the on-line interface. The evidence file for the problem can downloaded from this link.

Questions:

1. How would you write a Problog model for this task?
2. Do you find this kind of predictor satisfying? Would you call it “relational”?